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Undergraduate degree with 10+ years of Data Engineering experience with specific ETL Tools (e.g., DBT, Informatica, Data Stage, etc.) Bachelor of Engineering degree with 5-10+ years of Data Engineering experience with pertinent ETL tools (e.g., DBT, Informatica, Data Stage, etc.
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As a Data Engineering Team Lead at Dealer Tire, you will lead a highly skilled team of innovative data professionals who are responsible for designing and implementing our company and enterprise data lakes in Snowflake, datasets/data models in Power BI, and the ETL/ELT pipelines that feed our business and analytical systems.
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Experience in developing an end-to-end AWS native platform for building Data lakes (S3, Glue (Crawlers, ETL, Catalog), IAM, CodePipeline, CodeCommit, CloudFormation (or Terraform), CloudTrail, CloudWatch, AWS Config, Guard Duty, Secrets Manager, KMS, EC2, Data Visualization Tool: Tableau run on an EC2 or AWS Quicksight), Athena.
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Strong skills in languages such as Python, R, SQL or Scala, alongside experience of using modern and traditional data technologies including: ElasticSearch, MongoDB, PostgreSQL, mySQL/mariaDB, Oracle, SQL Server, Hadoop, Kafka, Splunk/ELK or other logging and monitoring tools, BI and Data Warehousing solutions and ETL and migration technologies.
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Stay abreast of the latest advancements in Databricks and other relevant technologies to continually enhance data engineering practices. Develop and maintain ETL processes to ingest, transform, and load data from various sources into Databricks Delta Lake.
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8+ years data engineering experience focused on Snowflake, Databricks, Kafka, AWS Glue, Airflow, and AWS services. As the Manager, Software Engineering - Data Engineering & Analytics team, you’ll lead a team data engineers in building and maintaining scalable, reliable, and efficient data pipelines using state-of-the-art technologies.
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Data ETL tools: SSIS, dbt, Data API, Matilion, Boomi, etc. Supervises and coaches junior data engineering support personnel and provides guidance and direction to third party contract developers to deliver fast and reliable data for advanced analytics solutioning.
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O Knowledge of modern data engineering practices including data pipeline & ETL tools, distributed storage & processing and data warehousing. We use Kafka for data pipelines, Apache Beam for ETL, Bitbucket for source control, Jenkins for continuous integration, Grafana + Prometheus for metrics collection, ELK for log shipping and monitoring, Docker for containerisation, Kubernetes for container orchestration, OpenStack for our private cloud, Ansible for architecture automation, and Slack for internal communication.
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3+ years of experience in data engineering, data integration, data modeling, data architecture, and ETL/ELT processes to provide quality data and analytics solutions.
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Bachelor's degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and five (5) years of experience as a Principal Data Engineer (or closely related occupation) designing, implementing, and developing applications in Sybase, SQL server, Oracle, Informatica, Python and Azure.
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Strong proficiency in programming languages and scripting tools used in data engineering (e.g., Python, SQL, Scala). Knowledge of data warehousing architectures, data integration techniques, and ETL tools (e.g., Informatica, Talend, Apache NiFi.
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Highly proficient working in Azure cloud environment (e.g. Blob / ADLS, Databricks, Azure Data Factory, Event Hub) BS, MS, or PhD in Computer Science, Information Technology, Management Information Systems (MIS), Data Science or related field.
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Experience with data engineering concepts and challenges including data warehouse solutions, ETL, and distributed systems, as well as familiarity with and experience manipulating data in various formats, such as: avro, neo4j, json, cef, csv, w3c, pandas.
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This role will serve as a hands-on contributor and be a thought leader in the areas of data engineering, cloud data strategy, Business Intelligence, Data Modeling and ETL/ELT.
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Knowledge of data engineering and data warehousing concepts, including RedShift, Redshift Spectrum, AWS Glue, AWS Lambda. Experience with data visualization tools such as Tableau, MixPanel, Google Looker.
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